Update README.md

README.md

@@ -8,16 +8,21 @@ license: apache-2.0
 
 **slim-sentiment-tool** is part of the SLIM ("Structured Language Instruction Model") model series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling.  
 
-slim-sentiment-tool is a 4_K_M quantized GGUF version of slim-sentiment-tool, providing a fast, small inference implementation.
+slim-sentiment-tool is a 4_K_M quantized GGUF version of slim-sentiment, providing a fast, small inference implementation.
 
-Load in your favorite GGUF inference engine, or try with llmware as follows:
+Load in your favorite GGUF inference engine (see details below on how to set up the prompt template), or try with llmware as follows:
 
     from llmware.models import ModelCatalog
 
-    sentiment_tool = ModelCatalog().load_model("llmware/slim-sentiment-tool")
-    response = sentiment_tool.function_call(text_sample, params=["sentiment"], function="classify")
+    # to load the model and make a basic inference
+    sentiment_tool = ModelCatalog().load_model("slim-sentiment-tool")
+    response = sentiment_tool.function_call(text_sample)  
 
-Slim models can also be loaded even more simply as part of LLMfx calls:
+    # this one line will download the model and run a series of tests automatically
+    ModelCatalog().test_run("slim-sentiment-tool", verbose=True)  
+    
+    
+Slim models can also be loaded even more simply as part of a multi-model, multi-step LLMfx calls:
 
     from llmware.agents import LLMfx
 
@@ -40,7 +45,18 @@ Slim models can also be loaded even more simply as part of LLMfx calls:
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-The intended use of SLIM models is to re-imagine traditional 'hard-coded' classifiers through the use of function calls.  
+The intended use of SLIM models is to re-imagine traditional 'hard-coded' classifiers by combining:  
+
+- LLM function calls
+  
+- Agents created with multiple models
+  
+- Small specialized models 'built for purpose'
+  
+- Quantization
+
+Please check out the config.json file included in the repository which includes details on the GGUF model, as well as a set of a 
+test samples. 
 
 Example: